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Error estimate of discriminant for normal distribution.

    E = TESTN(W,U,G,N)

 W Trained classifier mapping
 U C x K dataset with C class means, labels and priors (default: [0 .. 0])
 G K x K x C matrix with C class covariance matrices (default: identity)
 N Number of test examples (default 10000)

 E Estimated error


This routine estimates as good as possible the classification error  of Gaussian distributed problems with known means and covariances. N normally distributed data vectors with means, labels and prior  probabilities defined by the dataset U (size [C,K]) and covariance  matrices G (size [K,K,C]) are generated with the specified labels and are  tested against the discriminant W. The fraction of incorrectly classified  data vectors is returned. If W is a linear 2-class discriminant and N is not specified, the error is computed analytically.

See also

mappings, datasets, qdc, nbayesc, testc,

PRTools Contents

PRTools User Guide

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